RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing

K Georgiadis, FP Kalaganis, VP Oikonomou… - Brain Informatics, 2022 - Springer
Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of
conventional marketing tools, like questionnaires and focus groups …

A data augmentation scheme for geometric deep learning in personalized brain–computer interfaces

FP Kalaganis, NA Laskaris, E Chatzilari… - IEEE …, 2020 - ieeexplore.ieee.org
Electroencephalography signals inherently deviate from the notion of regular spatial
sampling, as they reflect the coordinated action from multiple distributed overlap** cortical …

Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization

FP Kalaganis, NA Laskaris… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. The wider adoption of Riemannian geometry in electroencephalography (EEG)
processing is hindered by two factors:(a) it involves the manipulation of complex …

[HTML][HTML] ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

SI Dimitriadis - Computers in Biology and Medicine, 2024 - Elsevier
Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia
(SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the …

[PDF][PDF] A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces

E CHATZILARI, S NIKOLOPOULOS - researchgate.net
Electroencephalography signals inherently deviate from the notion of regular spatial
sampling, as they reflect the coordinated action from multiple distributed overlap** cortical …